Search: “llm”

arXiv:2609.00868v1 Announce Type: cross Abstract: Vision-language models are evaluated by aggregate accuracy on multimodal benchmarks, a practice that implicitly assumes the model uses its visual inpu

arXiv:2609.00050v1 Announce Type: cross Abstract: Agentic AI is enabling cloud-based workflows in which autonomous agents reason over operational state, invoke authorized tools, modify software and in

arXiv:2609.00470v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) grounds large language models in external corpora, but implicit trust in retrieved documents creates a critical att

arXiv:2609.00921v1 Announce Type: new Abstract: Personalized Large Language Models (PLLMs) aim to tailor responses to individual users, where a central challenge is preference reasoning: inferring que

arXiv:2609.00570v1 Announce Type: new Abstract: With the growing scale of multi-agent architectures and large language models, deployed AI assistants are increasingly tasked with reasoning over long,

arXiv:2609.01030v1 Announce Type: cross Abstract: Price extraction from websites is a key task for market monitoring, price comparison, and business analytics in e-commerce. Existing approaches can be

arXiv:2609.00905v1 Announce Type: new Abstract: Sampling from distributions conditioned on desired semantic properties is an emerging challenge in modern generative modeling. Metropolis-Hastings (MH)

arXiv:2609.00309v1 Announce Type: cross Abstract: AI compute verification is one of the first tangible and tractable points for international policy aimed at AI governance. Determining whether frontie

arXiv:2608.28652v1 Announce Type: new Abstract: Artificial intelligence (AI) models have demonstrated remarkable capabilities across various domains, yet their widespread deployment is impeded by sign

arXiv:2608.29702v1 Announce Type: new Abstract: A token-embedding table holds a hub of short rows near its origin, and we show that this cluster biases what nearest-neighbor intrinsic-dimension (ID) e

arXiv:2608.30976v1 Announce Type: new Abstract: Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate domai

arXiv:2608.28990v1 Announce Type: new Abstract: Protein co-abundance clusters preserved across tissues can reveal shared disease mechanisms and candidate therapeutic targets, particularly when protein

arXiv:2608.30805v1 Announce Type: cross Abstract: Natural-language tasks can elicit different verdicts from protocol-following evaluators that receive the same declared information. We study aggregate

arXiv:2508.04865v4 Announce Type: replace Abstract: Large language models (LLMs) already excel at writing code in high-resource languages such as Python and JavaScript, yet stumble on low-resource lan

arXiv:2608.29175v1 Announce Type: new Abstract: Temporal inconsistencies, such as mandates attributed outside their real interval, events presented as past before they occurred, or inverted causal seq

arXiv:2606.15017v2 Announce Type: replace Abstract: Online web agents often augment a base actor with memory, workflow, or skill modules. These modules can improve performance, but they also consume t

arXiv:2608.31111v1 Announce Type: new Abstract: Many important forms of human learning begin with a vague goal, such as 'become a better physicist' or 'improve at research.' Learners must interpret th

arXiv:2506.03828v4 Announce Type: replace Abstract: AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows, such as condition monitoring and maintenance scheduling

arXiv:2608.30107v1 Announce Type: cross Abstract: Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and in

arXiv:2608.29111v1 Announce Type: cross Abstract: Applications such as personalized assistance and proprietary document analysis require large language models (LLMs) to generate outputs from private d

arXiv:2608.28632v1 Announce Type: new Abstract: Large language model agents can discover alphas, yet current methods have three weaknesses. The search cannot adapt during the run, automation usually e

arXiv:2510.03136v2 Announce Type: replace Abstract: Confidence calibration, the alignment of a model's predicted confidence with its actual accuracy, is crucial for the reliable deployment of Large La

arXiv:2608.30731v1 Announce Type: cross Abstract: Assessing claim check-worthiness is an essential first step in automated fact-checking pipelines. This work is motivated by a real deployment challeng

arXiv:2608.29460v1 Announce Type: new Abstract: When coding agents encounter defective test infrastructure they may reward-hack: hardcoding outputs or editing test files to pass tests they cannot legi

arXiv:2608.28649v1 Announce Type: cross Abstract: Touchpoint selection in conversion attribution, namely identifying meaningful touchpoints contributing to conversions, is essential for e-commerce rec

arXiv:2608.28809v1 Announce Type: new Abstract: Extreme low-bit inference offers a route toward smaller models and constrained deployment. Ternary language models restrict weights to {-1,0,+1}, approa

arXiv:2608.28628v1 Announce Type: new Abstract: Compound drought-to-extreme-precipitation (CDEP) events are recognized in climate science as a growing driver of extreme impact, but whether this recogn

arXiv:2608.29621v1 Announce Type: cross Abstract: Long-horizon story-driven video generation requires a production agent to coordinate narrative decomposition, state tracking, shot design, prompt cons

arXiv:2608.29284v1 Announce Type: new Abstract: Deploying large language models for legal question answering raises challenges that general-purpose leaderboards do not capture, particularly for low-re

arXiv:2608.30234v1 Announce Type: new Abstract: Automatic medical coding assigns ICD codes to clinical notes, but it remains challenging due to long documents, imbalanced label distributions, and dive

arXiv:2608.31170v1 Announce Type: new Abstract: While WhisperX accelerates speech transcription via intra-audio batching, it isolates audio segments, losing the historical context needed for coherent

arXiv:2510.22876v4 Announce Type: replace-cross Abstract: Inference optimizations are routinely evaluated by throughput alone, without verifying output correctness. We conduct a forensic analysis of b

arXiv:2608.29675v1 Announce Type: cross Abstract: Repository exploration is a distinct and costly stage of coding-agent pipelines: before generating a patch, an agent must identify which repository fi

arXiv:2608.29613v1 Announce Type: new Abstract: Function vectors (FVs) have recently emerged as a promising mechanism for steering the behavior of large language models (LLMs) by injecting task-specif

arXiv:2608.28691v1 Announce Type: cross Abstract: Wearable VLM pipelines promise continuous multimodal assistance from egocentric visual capture: a user asks a task-driven question about the surroundi

arXiv:2608.28824v1 Announce Type: new Abstract: Currently, in biological systems Neural Correlates of Consciousness (NCCs) are characterized in terms of EEG and FMRI signals. Unfortunately, this chara

arXiv:2608.28968v1 Announce Type: new Abstract: Semantic Search on LinkedIn must retrieve relevant profiles from a corpus of hundreds of millions in response to natural-language queries such as 'a fin

arXiv:2608.29035v1 Announce Type: new Abstract: Emotion recognition in conversations is increasingly tackled with language models, but these models can be unstable and expensive to fine-tune or to pro

arXiv:2608.29890v1 Announce Type: new Abstract: Biomedical Named Entity Recognition (NER) is fundamental to healthcare AI applications, including clinical decision support and medical information extr

arXiv:2608.29431v1 Announce Type: new Abstract: Localized Representation Steering (LRS) is widely used to correct reasoning pathologies in large language models. However, standard benchmark evaluation

arXiv:2608.29835v1 Announce Type: new Abstract: Large language models (LLMs) often produce fluent but weakly grounded conclusions when reasoning over non-interactive, long-form narratives. A central f

arXiv:2608.31066v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves multi-step problem solving, but long reasoning traces inflate inference cost. Token-level CoT compression redu

arXiv:2608.31014v1 Announce Type: new Abstract: Computational mental health screening using multimodal speech and text has shown great promise. However, existing models often assume all clinical speec

arXiv:2608.30996v1 Announce Type: new Abstract: Retrieval-augmented generation systems can precompute and store key-value caches of retrieved documents to avoid re-encoding context at every query. Qua

arXiv:2608.29372v1 Announce Type: new Abstract: Retrospective backtests provide a limited test of adaptive trading agents: they cannot rule out historical contamination, expose sensitivity to a single

arXiv:2608.28662v1 Announce Type: new Abstract: Fracture detection and its clinical interpretability see notable improvements when deep vision models are integrated with agentic AI architectures. Whil